The R package
loggle provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid.
loggle is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance, the gene regulatory networks over the course of organismal development, and the dynamic relationships between individuals in a community over a few years.
loggle estimates time-varying graphical models under the assumption that the graph topology changes gradually over time.
loggle has been applied to S&P 500 stock price dataset, where the interacting relationships among stocks and among industrial sectors in a time period that covers the recent global financial crisis can be revealed. Detailed description of S&P 500 stock price dataset is in
For more details on estimating time-varying graphical models and the package, please refer to: Estimating Time-Varying Graphical Models https://arxiv.org/abs/1804.03811.
Please make sure to install the following package dependencies before using R package
loggle. R with version later than 3.0.2 is needed.
install.packages(c("Matrix", "doParallel", "igraph", "glasso", "sm"))
The R package
loggle can be installed from source files in the GitHub repository (R package
devtools is needed):
loggle: learn time-varying graphical models for a given set of tuning parameters.
loggle.cv: conduct model selection via cross validation for learning time-varying graphical models.
loggle.cv.select: conduct model selection for time-varying graphical models based on cross validation results from
loggle.cv.vote: learn time-varying graphical models for a given set of tuning parameters via cv.vote.
loggle.refit: conduct model refitting given learned time-varying graph structures.
Please report any bugs to email@example.com.